[1]} proposed a multi-experts model named RIDE for long-tailed classification. Our xERM framework can be easily applied to RIDE to validate its effectiveness on ensemble models. Note that the implementation of re-balanced method is open in RIDE, thus we applied LDAM loss [2]} and PC to RIDE as our baseline, which are denoted as RIDE-LDAM-\(m\)  and RIDE-PC-\(m\)  (\(m\)  is the number of experts). The training settings follow [1]}. Our learned xERM models share the same model architecture with their balanced models, which are denoted as xERM\(_{\text{RIDE-LDAM-}m}\)  or xERM\(_{\text{RIDE-PC-}m}\) . Table REF  shows the results on balanced test set of CIFAR100-LT-IB-100, where our xERM models surpass their corresponding baselines in terms of accuracy, achieving new state-of-the-arts. Compared with baseline models, our xERM improves many-shot/medium-shot recall, medium-shot/few-shot precision and achieves competitive performance on few-shot recall and many-shot precision.
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